Power User Profiling Techniques for Fusing Heterogeneous Data from Multiple Sources

In order to realize the accurate classification and feature grasping of power users by the power supply department, this paper proposes a method of power user portrait by fusing heterogeneous data from multiple sources. The method first uses the K-means++ algorithm and combines the elbow rule to determine the optimal number of clusters K, and clusters the user load data and basic information separately; subsequently, the key load features are screened out by the mRMR method, and the radar diagram is used to visually display the features of different categories of users; finally, the refined construction of the user portrait is realized by combining various types of attribute labels, which provides the basis for the enterprise to provide personalized services. In this paper, the effectiveness of the proposed method is verified on two datasets.